Modern applications—whether traditional applications or DApps —have weak assumptions about time data, operating similarly to the early web 2.0 systems that worked without notification mechanisms. At the time, services such as Gmail and Orkut allowed users to perform an action such as sending an email and come back later to check the results of those actions.
While this has changed with modern push notification systems in centralized platforms, the unverifiability of time data persists. For example, traders often require accurate time data to conduct auction sales. This is not possible with the current centralized platforms because such systems usually rely on a facilitator (trusted third party) that is susceptible to fraud and single point of failures (SPOFs).
Blockchain has evolved rapidly since the unveiling of Bitcoin in 2008 because it relies on decentralized consensus to eliminate trusted third parties and SPOFs. Similarly, the number of use cases that rely on Blockchain has increased rapidly.
However, despite this growth, most players have largely focused on payments, decentralized finance (DeFi), exchanges, swaps, non-fungible tokens (NFTs), oracles, and gaming. Time data—a crucial component for managing workflows in these services—does not feature anywhere when it comes to Blockchain implementations.
The unverifiability of time data also extends to the current Blockchain implementations that operate best as first-to-file systems. For example, DeFi protocols such as Compound and Aave allow token holders to borrow loans and exchange other assets. However, there is no mechanism for token holders to learn about their loan liquidations in real-time enabled by verifiable time-data that can work across multiple chains.
Moreover, the Blockchain ecosystem lacks an interoperable, layer-0 protocol that can facilitate cross-chain interactions involving time data. The inability to share and synchronize time data across different networks has led to inefficient systems.
Take the example of an entity owning time data on chain A and looking to transact that data on chain B. There are two primary interoperability scenarios: the user could trade the time data on-chain A for another crypto asset on chain B or represent the time data on several chains and move instances from chain A to chain B.
In the first approach, the user could leverage a trusted third party (which goes against the spirit of decentralization) to exchange the time data. The user could also use built-in trust mechanisms such as atomic swaps to exchange the time data between the two protocols. While this approach seems viable, it does not provide a solution in a multi-chain environment.
To summarize, below are the main problems bedeviling the industry
Single point of failures. While a shared, global server can help determine the order of events with centralized synchronization approaches, the platform can suffer from a single point of failure (SPOF), leading to severe downtimes. Network attackers can exploit unauthenticated time data on a centralized platform and alter time data.
Presence of malicious nodes. Centralized systems rely on the questionable assumption that servers and clients are trustworthy. However, any malicious server or client that gains control over the network ecosystem can compromise the precision and accuracy of time data. Most architectures allow nodes to authenticate to servers via digital certificates. However, such a process requires more computational power that may not be feasible in various applications like the internet-of-things (IoT).
Fragmented approach to time data storage. Distributed applications need to synchronize their time data to achieve high throughput. When applications coordinate seamlessly, transactions take less time to be verified, meaning more transactions within a short time. This is not the case with current applications because of three reasons.
First, time data is inconsistent across different chains and applications. Because of varying data formats, businesses cannot trigger actions based on real-time data, leading to lost opportunities. Second, the time data landscape is fragmented, with the same data stored in different chains. This often leads to data inaccuracies, complicating communication processes.
Lastly, there is a lack of sharing mechanisms. The inability to share and synchronize time data across different chains leads to inefficient systems.
Lack of privacy mechanisms. Artificial intelligence (AI) and other mass data technologies have allowed businesses to make informed choices and grow exponentially. Privacy and security concerns have emerged because of the increased use of data. Users are increasingly sacrificing their privacy consent in exchange for recommendations and personalized assistance that centralized platforms usually provide.